<\/span><\/h2>\n\n\n\nAt its essence, a chatbot is designed to respond to a user request and, as such, are often used to provide a form of online chat support – and it does this in two main steps. <\/p>\n\n\n\n
First, the user request is analyzed. Arguably the most important and relevant step, this part of the process is where the key information of the request is highlighted and the user\u2019s true intent is deciphered. The second step is the response, where an answer or direction is given to the user\u2019s initial request. <\/p>\n\n\n\n
While always aiming to interact in a conversational and friendly way, the responses a chatbot gives are often rule-based. Rule-based chatbots, also known as declarative chatbots, are usually made for a single defined purpose. Using machine learning, an algorithm which allows them to learn from past interactions, these chatbots are trained to process information and form responses based on the unique information they are given. Through this process, chatbots are also trained to give responses that align with a brand\u2019s preferred tone of voice and match a target audience or customer-base. <\/p>\n\n\n\n
Using a system of pattern matching, natural language processing (NLP), tokenization and keyword identification, these bots have the ability to analyze a request and pull relevant information from a knowledge base to form an answer. This enables them to provide fast and effective support when faced with common queries. For instance, if a customer asks about an address, the chatbot can pick out the keyword \u2018address\u2019 or \u2018location\u2019 and direct the customer to an FAQ or resource hub that contains the right information. This process may come with some limitations, however, which is why chatbots often work best in conjunction with human agents. <\/p>\n\n\n\n
If a request is more complex or requires a more detailed and specific answer, chatbots can escalate to a human agent to resolve the issue.
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